The most similar approaches to the one proposed in this paper are Sun et al.  [1]} and Dubé et al.  [2]}, which realize semantic SLAM using only a single LiDAR sensor. Sun et al.  [1]} present a semantic mapping approach, which is formulated as a sequence-to-sequence encoding-decoding problem.
Dubé et al.  [2]} propose an approach called SegMap, which is based on segments extracted from the point cloud and assigns semantic labels to them. They mainly aim at extracting meaningful features for the global retrieval and multi-robot collaborative SLAM with very limited types of semantic classes.
In contrast to them, we focus on generating a semantic map with an abundance of semantic classes and using these semantics to filter outliers caused by dynamic objects, like moving vehicles and humans, to improve both mapping and odometry accuracy.
